TY - GEN
T1 - Segmentation and Recognition of Lung Adenocarcinoma Cells Based on U-Net Model
AU - Jin, Ziyang
AU - Zhang, Qing
AU - Sun, Zhen
AU - Li, Qingli
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Lung adenocarcinoma poses a great threat to human health, and early diagnosis is very important for treatment. Currently, pathologists analyze and diagnose pathological cells by observing their distribution (normal cells, hyperplasia and cancer cells). So, accurate segmentation of lung cells is very important to help pathologists make diagnosis. However, it is a heavy workload to obtain information from whole slide images by human eye observation. And with the development of deep learning, its application in medical image is increasing. We can segment and recognize cells based on this technology. U-Net model, one of the most classic segmentation models, has obtained enormous number of achievements in pathological image processing. Therefore, in this paper, we propose a segmentation model for lung adenocarcinoma cells based on U-Net model. This model is trained by synthesizing pseudo-color images generated from three bands of hyperspectral images as input. We have conducted experiments on a home-made lung adenocarcinoma dataset and the results show that this method can get precise segmentation results.
AB - Lung adenocarcinoma poses a great threat to human health, and early diagnosis is very important for treatment. Currently, pathologists analyze and diagnose pathological cells by observing their distribution (normal cells, hyperplasia and cancer cells). So, accurate segmentation of lung cells is very important to help pathologists make diagnosis. However, it is a heavy workload to obtain information from whole slide images by human eye observation. And with the development of deep learning, its application in medical image is increasing. We can segment and recognize cells based on this technology. U-Net model, one of the most classic segmentation models, has obtained enormous number of achievements in pathological image processing. Therefore, in this paper, we propose a segmentation model for lung adenocarcinoma cells based on U-Net model. This model is trained by synthesizing pseudo-color images generated from three bands of hyperspectral images as input. We have conducted experiments on a home-made lung adenocarcinoma dataset and the results show that this method can get precise segmentation results.
KW - cell segmentation
KW - computer-assistant diagnosis
KW - lung adenocarcinoma
UR - https://www.scopus.com/pages/publications/85183325019
U2 - 10.1109/CISP-BMEI60920.2023.10373327
DO - 10.1109/CISP-BMEI60920.2023.10373327
M3 - 会议稿件
AN - SCOPUS:85183325019
T3 - Proceedings - 2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2023
BT - Proceedings - 2023 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2023
A2 - Zhao, XiaoMing
A2 - Li, Qingli
A2 - Wang, Lipo
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2023
Y2 - 28 October 2023 through 30 October 2023
ER -
